Bridge afflux estimation using artificial intelligence systems

Seçkin, Galip; Çobaner, Murat; Özmen-Çağatay, Hatice; Atabay, Serter; Erduran, K. S.

doi:10.1680/wama.2011.164.6.283

Cited by 6 publications

(10 citation statements)

References 40 publications

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“…Three stages of the scouring process can be distinguished: the initial stage related to the scouring initiation, the proper stage, which is the period of scour development and deepening, and the final stage, in which the shape stabilization could be observed. The most significant initial stage could be pointed to, which is characterized by the high stream velocities occurrence in the bottom region and high stream turbulence [4][5][6][7][8]. The water surface elevation along the river reach depends not only on the hydraulic structure properties but also on the local granulometric conditions, which can be quantitatively characterized by hydraulic resistance coefficients.…”

Section: Introductionmentioning

confidence: 99%

Bridge headwater afflux estimation using bootstrap resampling method

2023

Archives of Civil Engineering

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The bridge structure's development causes a riverbed cross-sections contraction. This influences the flow regime, being visible during catastrophic floods. Then the flow velocity increases and water piles up upstream the bridge, where headwater afflux could be observed. These changes depend on the watercourse geometry and the bridge cross-section properties, especially on the degree of flow contraction under the bridge. Hydraulic conditions under the bridge depend on flow velocity, dimensions, and shape of abutments, the granulometric composition of bedload, which can be quantitatively characterized by hydraulic resistance coefficients. The research subject of headwater afflux is equated with the recognition of morphodynamic processes occurring along the passage route. The headwater afflux could be estimated by empirical formulas and by the energy method using Bernoulli's law. Empirical methods are optimized by adopting various statistical criteria. This paper compares the headwater afflux values calculated using two existing empirical formulas, Rehbock and Yarnell, and compares them with the results of laboratory tests. Following the assumption that the free water surface is influenced by flow resistance, an attempt was made to include friction velocity in the empirical formulas. Based on the Authors' database, the coefficients used were optimized using bootstrap resampling in Monte Carlo simulation. The analyses demonstrated that the formula best describing the phenomenon of headwater afflux upstream the bridge is an empirical formula built based on the historical Yarnell formula, which includes friction velocity value. The optimized equation provides an average relative error of 12.9% in relation to laboratory observations.

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Section: Introductionmentioning

confidence: 99%

Bridge headwater afflux estimation using bootstrap resampling method

2023

Archives of Civil Engineering

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“…From the type of bridge, they may be grouped as contributions focusing on bridges with either horizontal soffit [1] or arch deck [2][3][4]. From methodological point of view, some researches applied numerical [5][6], experimental [7][8], and data mining approaches [3,[9][10][11] to predict bridge backwater. Furthermore, it should be noted that most of methods estimating bridge afflux have been basically developed based on field or laboratory datasets.…”

Section: Introductionmentioning

confidence: 99%

“…In this regard, the limitations of the available explicit equations for predicting backwater depth include (1) they were not developed by powerful algorithms and (2) their accuracy is not enough to be applied in professional software for analyzing rivers and designing of hydraulic structures. In order to enhance the accuracy of backwater estimation, artificial intelligence (AI) models including radial basis function based neural network (RBNN), multi-layer perceptron (MLP), generalized regression neural networks (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) have been utilized for this purpose using experimental and field databases [3][4][10][11]. In spite of all these studies, an accurate explicit equation, which takes all involving variables into account and developed by powerful algorithms like GA, has not been proposed based on the current literature.…”

Section: Introductionmentioning

confidence: 99%

Bridge backwater estimation: A Comparison between artificial intelligence models and explicit equations

2020

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Estimation of bridge backwater has been one of practical challenges in hydraulic engineering for decades. In this study, Genetic Programming (GP) has been applied for estimating bridge backwater for the first time based on the conducted literature review. Furthermore, two new explicit equations are developed for predicting bridge afflux using Genetic Algorithm (GA) and hybrid MHBMO-GRG algorithm. The performances of these models are compared with Artificial Neural Network (ANN) and several explicit equations available in the literature considering both laboratory and field data. Based on five considered performance evaluation criteria, the two new explicit equations outperform the ones available in the literature. Furthermore, GP and ANN achieve the 2 best results in favor of four out of five considered criteria for train and test data, respectively. To be more specific, ANN improves the MSE and R 2 values of the explicit equation developed using GA by 44% and 12% for the test data while GP enhances the corresponding values by 62% and 9% for the train data. Finally, the results demonstrate that not only artificial intelligence models considerably improve bridge afflux estimation than the explicit equations but also the suggested equations significantly improve the accuracy of the available explicit ones.

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“…Multiple linear regression (MLR) and two ANN models were used for initial assessment of bridge backwater level (Cobaner et al, 2008;Seckin et al, 2011). The MLR and the ANN models were applied to the same set of data that was generated from experimental tests conducted at HR Wallingford in the UK.…”

Section: Introductionmentioning

confidence: 99%

Prediction of backwater level of bridge constriction using an artificial neural network

Atabay¹,

Abdalla²,

Erduran³

et al. 2013

Proceedings of the Institution of Civil Engineers - Water Manag

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Bridge constriction in channels usually increases the water level well above the normal depth and may result in overflow on the surrounding floodplain. In this paper, the experimental backwater level at which the maximum afflux value was observed due to bridge constriction was investigated. An artificial neural network (ANN) was used to predict the backwater level based on Manning's roughness coefficient of the main channel (nmc) and of the floodplain (nfp), bridge width (b) and flow discharge (Q). A multi-layer perceptron (MLP) ANN was used to predict the backwater level using these parameters. Multiple linear (MLR) regression and multiple non-linear regression (MNLR) were used as benchmarks for comparison of ANN results. It is concluded that an ANN can very accurately predict the backwater level. The developed ANN model was then used to conduct a parametric study to investigate the influence of nmc, nfp, b and Q on the backwater level due to a bridge constriction without piers. It is concluded that nmc and Q have a more profound effect on the backwater level than does nfp, while b has very little effect on the backwater level within this range of parameters. Other observations and conclusions are also drawn.

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Bridge afflux estimation using artificial intelligence systems

Cited by 6 publications

References 40 publications

Bridge headwater afflux estimation using bootstrap resampling method

Bridge headwater afflux estimation using bootstrap resampling method

Bridge backwater estimation: A Comparison between artificial intelligence models and explicit equations

Prediction of backwater level of bridge constriction using an artificial neural network

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